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    <abstract>This paper describes our submission to the TL;DR challenge. Neural abstractive summarization models have been successful in generating fluent and consistent summaries with advancements like the copy (Pointer-generator) and coverage mechanisms. However, these models suffer from their extractive nature as they learn to copy words from the source text. In this paper, we propose a novel abstractive model based on Variational Autoencoder (VAE) to address this issue. We also propose a Unified Summarization Framework for the generation of summaries. Our model eliminates non-critical information at a sentence-level with an extractive summarization module and generates the summary word by word using an abstractive summarization module. To implement our framework, we combine submodules with state-of-the-art techniques including Pointer-Generator Network (PGN) and BERT while also using our new VAE-PGN abstractive model. We evaluate our model on the benchmark Reddit corpus as part of the TL;DR challenge and show that our model outperforms the baseline in ROUGE score while generating diverse summaries.</abstract>
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%0 Conference Proceedings
%T VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization
%A Choi, Hyungtak
%A Ravuru, Lohith
%A Dryjański, Tomasz
%A Rye, Sunghan
%A Lee, Donghyun
%A Lee, Hojung
%A Hwang, Inchul
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F choi-etal-2019-vae
%X This paper describes our submission to the TL;DR challenge. Neural abstractive summarization models have been successful in generating fluent and consistent summaries with advancements like the copy (Pointer-generator) and coverage mechanisms. However, these models suffer from their extractive nature as they learn to copy words from the source text. In this paper, we propose a novel abstractive model based on Variational Autoencoder (VAE) to address this issue. We also propose a Unified Summarization Framework for the generation of summaries. Our model eliminates non-critical information at a sentence-level with an extractive summarization module and generates the summary word by word using an abstractive summarization module. To implement our framework, we combine submodules with state-of-the-art techniques including Pointer-Generator Network (PGN) and BERT while also using our new VAE-PGN abstractive model. We evaluate our model on the benchmark Reddit corpus as part of the TL;DR challenge and show that our model outperforms the baseline in ROUGE score while generating diverse summaries.
%R 10.18653/v1/W19-8664
%U https://aclanthology.org/W19-8664
%U https://doi.org/10.18653/v1/W19-8664
%P 510-515

Markdown (Informal)

[VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization](https://aclanthology.org/W19-8664) (Choi et al., INLG 2019)

• VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization (Choi et al., INLG 2019)

ACL

• Hyungtak Choi, Lohith Ravuru, Tomasz Dryjański, Sunghan Rye, Donghyun Lee, Hojung Lee, and Inchul Hwang. 2019. VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization. In Proceedings of the 12th International Conference on Natural Language Generation, pages 510–515, Tokyo, Japan. Association for Computational Linguistics.